code: def register_report(name, implementation):
      """
      Register a custom reporting function to be used during eval.
  
      This can be useful:
      - if you want to overwrite a report for an existing output type of prediction head (e.g. "per_token")
      - if you have a new type of prediction head and want to add a custom report for it
  
      :param name: This must match the `ph_output_type` attribute of the PredictionHead for which the report should be used.
                   (e.g. TokenPredictionHead => `per_token`, YourCustomHead => `some_new_type`).
      :type name: str
      :param implementation: Function to be executed. It must take lists of `y_true` and `y_pred` as input and return a
                             printable object (e.g. string or dict).
                             See sklearns.metrics.classification_report for an example.
      :type implementation: function
      """
      registered_reports[name] = implementation
  
imports: import logging
  import numpy
  import torch
  from functools import reduce
  from scipy.stats import pearsonr, spearmanr
  from seqeval.metrics import classification_report
  from seqeval.metrics import f1_score
  from sklearn.metrics import matthews_corrcoef, f1_score, mean_squared_error, r2_score, classification_report
  from farm.utils import flatten_list
label_desc: Use image node_id 2 for calling a global function with example usage: register_report(name, implementation) without return types